--- title: Recreating Steffl's Calib keywords: fastai sidebar: home_sidebar summary: "Based on his thesis appendix" description: "Based on his thesis appendix" nb_path: "notebooks/06_calib.steffl.ipynb" ---
steffl_spica_dates[2]
filter_spica_for_date(steffl_spica_dates[2])
obsids = filter_spica_for_date(steffl_spica_dates[1]).filename.values
obsids
for id in obsids:
try:
data = UVPDS(id)
except FileNotFoundError:
print(id, "not there.")
else:
print("Got", id)
import hvplot.xarray
kwargs = {"x": "nx", "y": "ny", "cmap": "viridis", "clim": (0, 50)}
d1 = EFUV_PDS("EUV2002_198_04_31")
d2 = EFUV_PDS(obsids[3])
d1.label
d1arr = d1.xarray.astype("int16")
d2arr = d2.xarray.astype("int16")
diff = d2arr - d1arr
background = d1arr.sel(ny=slice(50, 60), nx=slice(60, 70), nz=1, drop=True)
background
background.hvplot(x="nx", y="ny", aspect=2)
bps = background / 332
bps.hvplot.hist()
bps.mean()
class FlatFielder:
def __init__(self, pid):
self.pid = pid
self.data = EFUV_PDS(pid).xarray.astype("int16")
@property
def integrated(self):
return self.data.sum(dim="nz")
@property
def plot_integrated(self):
return self.integrated.hvplot(x="nx", y="ny", cmap="viridis")
@property
def averaged(self):
return self.integrated.sel(ny=slice(2, 60)).mean(dim="ny")
@property
def plot_averaged(self):
return self.averaged.hvplot(x="nx")
@property
def ff(self):
return self.integrated / self.averaged
@property
def plot_ff(self):
return self.ff.hvplot(x="nx", y="ny", cmap="viridis")
pid = "EUV2002_198_04_31"
pid = "EUV2003_139_21_00_38"
pid = "FUV2002_198_04_31"
pid = "FUV2003_139_21_00_38"
flatter = FlatFielder(pid)
flatter.plot_integrated
flatter.plot_averaged
flatter.data.hvplot(x="nx", y="ny", cmap="viridis")
flatter.plot_ff
diff.hvplot(x="nx", y="ny", cmap="viridis", clim=(-100, 100))
d1.xarray.hvplot(x="nx", y="ny", cmap="viridis", clim=(0, 50))
d2.xarray.hvplot(x="nx", y="ny", cmap="viridis", clim=(0, 50))
(
arr.hvplot(x="nx", y="ny", cmap="viridis", clim=(0, 50))
+ arr.hvplot.quadmesh(x="nx", y="ny", cmap="viridis", clim=(0, 50))
).cols(1)
euv.integration_duration
diff = arr.nx.diff("nx")
diff.name = "diff"
diff.hvplot.hist()
fuv = FUV_PDS(UVISOpus(obsids[3][:-3]).local_label_path)
fuvarr = fuv.xarray
fuvarr.hvplot(x="nx", y="ny", clim=(0, 50), cmap="viridis")
euv.label
archive_df.loc["EUV2001_093_08_35_28"]
import hvplot.xarray
import xarray as xr
ds = xr.open_dataset(fname)
ds
np.percentile(ds.window_0, (5, 95))
ds.window_0.hvplot.image(
x="spectral_dim_0", y="integrations", cmap="viridis", clim=(0, 201)
)
ds.window_0.mean("integrations").hvplot(x="spectral_dim_0")
ds.window_0.plot()
p = obsdir / "index_repaired.tab"
df = pd.read_csv(p, quotechar='"', skipinitialspace=True)
from planetarypy.pds.indexes import find_mixed_type_cols
find_mixed_type_cols(df, fix=False)
find_mixed_type_cols??
df.columns
index.columns
index[index.filename.str.startswith("EUV")].iloc[0]
obs.head()
cols = ["index start_time stop_time detector target obsid_time unknown type comment "]